NaiveBayes

class capymoa.classifier.NaiveBayes

Bases: MOAClassifier

Naive Bayes incremental learner.

Performs classic Bayesian prediction while making the naive assumption that all inputs are independent. Naive Bayes is a classifier algorithm known for its simplicity and low computational cost. Given n different classes, the trained Naive Bayes classifier predicts, for every unlabeled instance I, the class C to which it belongs with high accuracy.

>>> from capymoa.classifier import NaiveBayes
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = NaiveBayes(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
84.8

__init__(

schema: Schema | None = None,

random_seed: int = 0,

)

cli_help()

predict(instance)

Predict the label of an instance.

The base implementation calls predict_proba() and returns the label with the highest probability.

Parameters:

instance – The instance to predict the label for.

Returns:

The predicted label or None if the classifier is unable to make a prediction.

predict_proba(instance)

Return probability estimates for each label.

Parameters:

instance – The instance to estimate the probabilities for.

Returns:

An array of probabilities for each label or None if the classifier is unable to make a prediction.

train(instance)

Train the classifier with a labeled instance.

Parameters:

instance – The labeled instance to train the classifier with.

random_seed: int

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

schema: Schema

The schema representing the instances.